Coffee-Heart Rate Research Paper

1564 words 7 pages

Part 1 The study used for this project is a nursing and health sciences study. One particular healthcare facility is trying to determine if it is a good idea to provide coffee in the waiting room for the patients. There are several other facilities that serve tea, coffee, and water, so this health care facility wants to determine if there is sufficient evidence to show that coffee increases the patient heart rates.
The question that I as a researcher want to answer is whether ingesting coffee, increases ones heart rate. The independent variable of this study is Coffee, and the dependent variable in this study is Heart Rate. The confounding or lurking variables in
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Because our study is only interested in an increase in the heart rate our testing will be a one tailed test. Because we are using a one-tail test we need to get our T score. To get that we need our degrees of freedom (n-1) and our alpha score. As Triola (2012) noted, our T score will be 1.761. Since we do not have the Standard Deviation of the Population we will need to use the T Score to get our Margin of Error. The formula to get the margin of error first requires that we choose a Confidence Level. The level we chose was 95% and the Compliment to that percentage is 05%. Using the formula TαSn-1 where T = 1.761 (95%) S = 11.4 and N = 15 we find that our “E” or Margin of Error is equal to 5.37 and this is for our before coffee stats. Using the same formula and our after coffee stats we find the Margin of Error is 5.13 after coffee. Using these margins of error we can plug them into the formula X –E < μ <X +E. We can Infer that the heart rate of the population will sit between 73 and 84 (rounded) before coffee, and after coffee the heart rate of the population will sit between 79 and 85 (rounded). In order to truly test our hypothesis, we need to see how the change is affecting the heart rate. To do this, first we calculate the difference between the before and after coffee numbers. Once that is done we need to get the Mean for the change. To do this, we add up all the change numbers and divide by the number of data points (in this


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